In image processing, it is common to compress image data so as to transmit the image data in a more efficient form. Compression may be mathematically lossy, in which some of the image data is discarded permanently. Compression may also be mathematically lossless, in which compression solely depends on the information content, or entropy, of the data, and perfect reconstruction of the original data from the compressed data can be achieved.
Most compression processes include the concept of a bit rate, and with respect to an image to be compressed, these processes permit the setting of a target bit rate. For example, if the target bit rate is set at a relatively low number of bits per pixel, then the image data may be compressed more aggressively, yielding a relatively small file that may be mathematically lossy. Conversely, if the target bit rate is set at a relatively high number of bits per pixel, then the image data may be compressed less aggressively, yielding a relatively large file that may be mathematically lossless. In view of these compromises, ongoing efforts have been directed at rate-distortion optimization, i.e., optimizing the amount of distortion (loss of image quality) against the amount of data required to encode the image (the bit rate).